The CVS method uses actual precinct votes in each county. The data is sorted by precinct size. The votes and shares are accumulated and displayed graphically. Typically, in the biggest counties, Democratic shares peak at the 10% CVS mark and decline at the final 100% (recorded vote). This is counter-intuitive because a) the most populous counties are in urban locations which are strongly Democratic and b) as the number of votes are accumulated, the Law of Large Numbers (LLN) should result in a Steady state of equibrium in which Democratic and Republican vote shares are nearly constant.

Click these links to view the summary 2014 CVS analysis (each has a link to the precinct votes for each county): ILFLWIMDMAKY

The True Vote Model (TVM)

The 2012 presidential election is used as a basis for returning 2014 voters. There are two options for estimating returning voters: the Recorded Vote and estimated True Vote.

The TVM closely matched the CVS in all governor elections except for Maryland. Hogan(R) won the recorded vote by 51.0- 47.2%, a 66,000 vote margin. Brown(D) won the True Vote by 56.4-41.9%, a 251,000 margin. The CVS analysis understates Brown’s vote since precinct votes were provided only for Election Day; early, provisional and absentee precinct voting were not included. This omission dramatically reduced Brown’s CVS since he had 54% of the excluded votes. Click these links to view the 2014 Governor True Vote Model: MDILFLWIKYMAMEOHKSMIGACO

In each election cycle, the Census Bureau interviews 60,000 households nationwide to estimate how many were registered and voted in each state.The national margin of error (MoE) is 0.3% for 60,000 respondents at the 90% confidence level. The MoE is approximately 2% for each state.

In 2014, 92.2 million votes were cast but just 78.8 million recorded. The 13.4 million discrepancy (14.5%) was greater than in any presidential election. What is going on here?

In every one of the 1968-2012 presidential elections, votes cast exceeded the recorded vote. The percentage of uncounted votes has declined steadily since 1988, from 10.4% to 2.9% in 2012. Uncounted votes peaked at 10.6 million in 1988 and declined to near zero in 2008. Approximately 75% of uncounted votes were Democratic (50% in minority locations).

The recorded vote was adjusted to total votes cast by adding the uncounted votes. The majority (60-75%) of uncounted votes were assumed to be Democratic, based on the historical fact that approximately 50% of uncounted votes are in minority locations.

The simplest measure of party strength in a state’s voting population is the breakdown-by-party totals from its voter registration statistics from the websites of the Secretaries of State or the Boards of Elections. As of 2014, 28 states and the District of Columbia allow registered voters to indicate a party preference when registering to vote.

In 2014, the party voter preference/registration split was 40.5D-35.3R-24.2I. The 2014 National Exit Poll indicated a 35D-36R-28I Party-ID split in forcing a match to the recorded vote (Dem 46.2-Rep 52.9%). Assuming the voter registration split and the Party-ID vote shares, the Democrats and Republicans were essentially tied.

The registered voter split for the 12 Governor elections in this analysis was 40.6D-34.4R-24.4I, a very close match to the national split.

The partisan “demographics” were obtained from the state’s party registration statistics (in late 2014 whenever possible). For the 22 states that don’t allow registration by party, Gallup’s annual polling of voter party identification is the next best metric of party strength.

Matching the Recorded and True Vote using the Party Registration split

To match the recorded vote, an implausibly low percentage of Democrats had to have voted for the Democratic candidate. Note the difference between the percentage of Democrats required to match the recorded vote and True vote shares. Democratic and Republican candidates usually win approximately 90-92% of registered Democrats and Republicans, respectively.

Percentage Share of Registered Democrats Required to Match

Match

Recorded Vote

True Vote

Match

Recorded

True Vote

MA

58.5

83.9

ME

66.6

92.0

MD

68.9

84.9

OH

53.5

92.1

KY

72.4

81.7

KS

86.1

95.5

WI

88.8

94.7

MI

75.5

88.1

FL

81.3

88.1

GA

87.7

93.3

IL

83.0

91.0

CO

88.6

93.8

KENTUCKY

Conway (D) lost the recorded vote by 52.5-43.8% despite the fact that the Democrats led 53.4-38.8% in voter registration. Bevin needed an implausible 24.6% of Democrats to match the recorded vote. Assuming just 81.7% of Democrats voted for Conway, he won by 48.8-47.5%, closely matching the CVS and True Vote.

According to the 2014 Census, 1.525 million total votes were cast in 2014. In 2015, Conway won by 49.3-47.0% – assuming he had 60% of an estimated 50,000 uncounted votes.

Of 2,298,000 registered voters, 974,000 (42.4%) voted. If 39% of Democrats voted and Conway had 88%, he won by 49.0-47.3%. If 43% of Democrats voted, he won by 54-42.3%.

Party Reg

Split

Conway

Bevin

Curtis

Democrat

53.4%

72.4%

24.6%

3%

Republican

38.8%

4%

92%

4%

Other

7.8%

46%

47%

7%

Recorded

100%

43.8%

52.5%

3.7%

Votes (000)

974

427

511

36

Party Reg

Split

Conway

Bevin

Curtis

Democrat

53.4%

81.7%

15.3%

3%

Republican

38.8%

4%

92%

4%

Other

7.8%

46%

47%

7%

True Vote

100%

48.8%

47.5%

3.7%

Votes (000)

974

475

463

36

Votes Cast

Total

Conway

Bevin

Curtis

Recorded

974

426.6

512.0

36.0

Uncounted (est)

50

30.0

18.2

1.9

2014 Census

1024

505

481

38

Adj. Share

49.3%

47.0%

3.7%

Party Reg

Split

Conway

Turnout 39%

41%

43%

Democrat

53.4%

88%

18.3%

19.3%

20.2%

Repub

38.8%

6%

0.9%

1.0%

1.0%

Other

7.8%

50%

1.5%

1.6%

1.7%

Conway

49.0%

51.5%

54.0%

Bevin

47.3%

44.8%

42.3%

MARYLAND

Hogan (R) won the recorded vote by 51.0-47.3%. Brown(D) had 53.7% of early votes, 45.3% on Election Day and 54.5% of absentee and provisional ballots. Precinct votes on touchscreens and optical scanners were provided for Election Day only.

When 295,000 uncounted votes are added to the recorded vote, Brown is the winner by 51.2-47.0% . When Election Day CVS at the 10% mark is added to the 390,000 early, absentee and provisional votes, Brown is a 52.9-45.5% winner.

– Kathy Dopp is a mathematician and an expert on election auditing. She has written a comprehensive analysis of the 2014 elections:Were the 2014 United States Senatorial and Gubernatorial Elections Manipulated? Dopp wrote:Is it possible electronic vote-count manipulation determines who controls government in the United States? The probability that the disparities between predicted and reported 2014 election vote margins were caused by random sampling error is virtually zero. A method for extending and simplifying fuzzy set qualitative comparative analysis (FsQCA)’s measure for necessity reveals that lack of effective post-election audits is a necessary condition for the occurrence of high levels of disparity between statewide polls and election results. Maryland’s 2014 gubernatorial contest is consistent with an explanation of vote miscount having altered its outcome. An analysis of Maryland’s partisan voter registration, turnout, and vote data by ballot type statistically confirms vote miscount as an explanation for its unexpected outcome.

Maryland, Illinois, Florida, and Kansas gubernatorial contests exhibited sufficient disparities between polls and election results (PED) to alter election outcomes; all used inauditable voting systems or failed to conduct post-election audits (PEA)s. Vermont’s PED was within one percent of sufficient to alter its outcome. In Nevada, Tennessee, New York, Ohio, and South Dakota PED were large but smaller than winning margins.

Kansas and North Carolina senatorial contests exhibited sufficient PED to alter election outcomes and no audits were conducted. Virginia’s PED was within one percent of sufficient to alter its outcome. In Arkansas, Wyoming, Tennessee, Kentucky, and Nebraska PED magnitude were large but smaller than winning margins.

A case study of Maryland’s unexpected 2014 gubernatorial outcome affirms there is, as yet, only an explanation of vote manipulation consistent with the statistical disparity patterns in Maryland’s pre-election poll predictions, and its partisan voter registration, turnout and vote data by ballot type.